Random Search

Introduction

The random search explores by choosing a new position at random after each iteration. Some random search implementations choose a new position within a large hypersphere around the current position. The implementation in hyperactive is purely random across the search space in each step.

Example

from gradient_free_optimizers import RandomSearchOptimizer

...

opt = RandomSearchOptimizer(search_space)
opt.search(objective_function, n_iter=100)

About the implementation

The random search is a very simple algorithm that has no parameters to change its behaviour. In each iteration the random position is selected via random.choice from a list of possible positions.